Abstract 14963: Accelerometer-measured Activity in Non-obstructive Hypertrophic Cardiomyopathy: Patient-generated Activity Measures Correlate With, and Are Convolutional Neural Network Predictors of, Clinical Parameters in the MAVERICK-HCM Study

Abstract

Introduction: Overall activity characteristics for patients with hypertrophic cardiomyopathy (HCM) have not been quantified previously. The relationship between physical activity quantified by accelerometry and biomarkers, exercise capacity, and quality of life in patients with HCM is also unknown. Methods: MAVERICK-HCM was a double-blind, placebo-controlled, 16-week study of mavacamten in 59 patients with symptomatic non-obstructive HCM. Patients were asked to wear ActiGraph GT9X Link wrist-worn monitors for ≥11 days between screening and day 1, and between weeks 12 and 16. Features derived from raw accelerometry data included average daily accelerometer units (ADAU) and step count. Univariate Pearson correlation coefficients were calculated between accelerometry data and clinical parameters among all patients. A multi-task convolutional neural network (CNN) was trained on raw accelerometry datapoints to jointly predict clinical markers of HCM severity. Test and training sets were derived by randomly segmenting each patient’s triaxial accelerometry data into non-overlapping minute intervals. Results: Fifty patients wore the accelerometer for ≥1 compliant day. Mean wear time was 12 days during screening and 10 days during treatment. Activity measures are summarized and average step count was 3,076 steps at baseline ( Table ). Activity features correlated with peak oxygen uptake (pVO 2 ), log NT-proBNP, and KCCQ score ( Table ). CNN predictions of clinical measures from activity data found Spearman R correlations of 0.82 for pVO 2 , 0.92 for log NT-proBNP, 0.82 for KCCQ, and 0.79 for E/e’. Conclusions: HCM patients in the MAVERICK study averaged only 3,000 steps/day. Markers of physical activity drawn from accelerometry are associated with standard clinical markers of HCM severity. Deep learning models can be constructed to predict markers of HCM severity from patients’ raw accelerometry data.